GIS for Planner Notes PDF

UP 418 – GIS for Planner Notes Class1: Definition and Components of GIS GIS is a computer-based system to aid in the collection, maintenance, storage, analysis, output, and distribution of spatial data and information. GIS (Geographic Information Systems) combines spatial data with other attribut...

UP 418 – GIS for Planner Notes Class1: Definition and Components of GIS GIS is a computer-based system to aid in the collection, maintenance, storage, analysis, output, and distribution of spatial data and information. GIS (Geographic Information Systems) combines spatial data with other attributes to perform four main functions: 1. Capture/Record 2. Store 3. Analyze 4. Visualize Key components of modern GIS include: 1. People 2. Data 3. Hardware 4. Software 5. Analytical Methods and Techniques Types of Data in GIS Spatial Data: - Shape - Size - Area Other Attributes: - Name and type - Function (e.g., road, building type) - Performance metrics (e.g., capacity, speed limit) - Socioeconomic data (e.g., income, education) - Environmental data (e.g., air quality, crop yield) Evolution of GIS - Originated in Canada in the 1960s for land suitability studies - First efforts to digitize maps and store spatial datasets - Initially required powerful mainframe computers - Introduced concepts like layers, digitized spatial features, and linked attribute databases GIS Capabilities 1. Capture/Record: Manual and automatic data collection 2. Store: Complex databases integrating spatial and other attributes 3. Analyze: Combine spatial and social data for sociospatial analysis 4. Visualize: Produce maps and other visualizations to display results GIS is crucial for urban planners and policymakers for: 1. Analyzing spatial patterns and relationships 2. Visualizing complex data 3. Making informed decisions based on spatial analysis 4. Identifying trends and patterns 5. Communicating ideas through maps and visualizations Practical applications include: - Land use planning and zoning - Transportation network analysis - Environmental impact assessments - Demographic studies and social equity analysis - Urban growth modeling - Emergency response planning - Resource allocation and management Important Skills for Planners 1. Understanding GIS relevance in planning and policymaking 2. Mastering techniques for spatial and social analysis 3. Applying critical principles of mapping and visualization 4. Proficiency in using ArcGIS Pro for real-world problems 5. Utilizing documentation to expand software knowledge Current Trends - 3D conceptualization of zoning and land use allocation - Integration of GIS with urban policy development - Applications in studying urban transportation, migration, and climate change impacts Class 2&3 - Earth's Shape and Representation Describing Location - Use latitude (degrees North/South) and longitude (degrees East/West) - Coordinates provide a precise way to identify any point on Earth's surface Earth's Irregular Shape - Earth is not a perfect sphere, but an irregular shape - Challenges in representation: 1. 3D complexity of the Earth's surface 2. Need for a calculable model to fit this complexity 3. Converting 3D positions to 2D maps for practical use Geoid - A smooth model based on mean sea level and constant gravity - Characteristics: - Assumes a constant gravity value - Traces a smooth, mean sea level partially following the actual Earth - Multiple possible geoids depending on chosen gravity constant, sea level, 3D center, and other reference points Ellipsoid - Mathematical model of Earth as an oblate spheroid - Derived from the geoid to create a best-fit model - Makes every point calculable through equations - Multiple possible ellipsoids correspond to different geoids Datums and Coordinate Systems Datums - Match mathematical ellipsoid with actual Earth locations - Combine ellipsoid model with ground survey measurements - Examples: - NAD83 (North American Datum of 1983) - WGS84 (World Geodetic System of 1984) Vertical Datums - Measure height and topography - Examples: - NGVD29 (National Geodetic Vertical Datum of 1929) - previous - NAVD88 (North American Vertical Datum of 1988) - current Height Measurements - Orthometric height (H): Topographic height – Geoid height - Ellipsoidal Height (h): Topographic height - Ellipsoid height - N: Difference between orthometric and ellipsoidal heights Map Projections Definition and Purpose - Systematic rendering of locations from curved Earth surface onto a flat map surface - Represent 3D Earth on 2D surface - Measure distance, area, and other spatial metrics - Maintain some uniformity in measurements within the same projection Characteristics of Projections 1. Conformal: Preserve shape 2. Equivalent: Preserve relative sizes 3. Equidistant: Preserve distances between various points 4. Azimuthal: Preserve certain directions and angles Types of Projections 1. Planar (Azimuthal) - Longitudes: Straight, radiating lines - Latitudes: Circles - Most accurate for polar regions - Distortions increase away from the poles - Limited to half the world 2. Conic - Longitudes: Straight, converging lines - Latitudes: Curves - Most accurate where the cone touches the surface (standard parallel) - Significant distortions in the opposite hemisphere 3. Cylindrical - Straight, perpendicular latitudes and longitudes - Good for low latitude areas - Greatest distortion near the poles - Useful for navigation (e.g., Mercator projection) Common Projections - UTM (Universal Transverse Mercator) - Transverse (rotated 90°) cylindrical projection - Divides the world into 60 Zones, North or South - Minimizes distortion in each region - Local shapes and angles are true - State Plane Coordinate System - 124 geographic zones for specific US regions - Combines transverse Mercator (N-S) and Lambert conformal conic (E-W) - Effective for city- and county-scale mapping Map Types and Data Representation Map Types 1. Reference maps 2. Isarithmic maps 3. Thematic maps - Choropleth maps - Dot Density maps - Cartograms 4. Flow maps Data Types - Qualitative: - Nominal (unique values, e.g., names) - Ordinal (relative values, categories) - Quantitative: - Interval (continuous, no true zero) - Ratio (continuous, zero is possible, e.g., population density) Classification Strategies for Quantitative Data 1. Equal interval - Equal difference between each interval - Example: Year of college (1-4) 2. Quantiles  - Distribute by frequency – equal frequency in each interval - Example: Income levels often measured in quantiles (Quartiles/Quintiles) 3. Mean/Standard Deviation - Based on distribution curve - Example: Student test scores - Each 0.5 or 1 standard deviation counts as an interval 4. Natural Breaks (Jenks) - Use algorithms to find natural intervals - Suitable for data not normally distributed Map Design and Elements Color Schemes - Qualitative: For nominal data - Sequential: For ordinal, interval, or ratio data showing progression - Diverging: For data with a meaningful midpoint and extremes Essential Map Elements 1. Title: Summarizes the purpose of the map 2. Legend: - Order matters: Point → Line → Polygon - Urban → Rural - Important → Less Important 3. Map Body: The main map itself (keep it simple) 4. Scale: Suited to purpose, consistent across multiple maps 5. Labels: Ensure readability 6. Direction: North arrow 7. Source: Data attribution Design Principles - KISS: Keep It Simple, Stupid! - Know your audience - Include all important elements - Ensure visual hierarchy and balance Week 4: Types of Spatial Data Raster Data - Represents continuous data without discrete boundaries - Composed of a grid of cells (pixels), each containing data - Smaller pixels provide greater detail but require more storage - Examples: aerial photographs, satellite images, elevation surfaces Vector Data - Represents discrete features with defined boundaries - Stored as coordinate pairs with attribute tables - Three main types: 1. Points: Single coordinate pairs (x,y) with no length or area 2. Lines: Series of connected points with length but no width or area 3. Polygons: Closed series of connected lines with area Geodatabase A geodatabase is a specialized database designed to store, organize, and manage geographic information. Key Features - Supports multiple users - Enables efficient data retrieval - Allows for complex queries - Handles various geographic data formats (e.g., shapefiles) Types of Geodatabases 1. Personal Geodatabase 2. File Geodatabase 3. Enterprise Geodatabase Data Organization within a Geodatabase Tables - Rows represent individual entries - Columns contain attributes Feature Classes - Collection of geographic features (points, lines, or polygons) - Each row is an individual feature - Columns contain attributes, including spatial attributes: - SHAPE: point, polyline, or polygon - SHAPE_LENGTH: Perimeter length (not applicable for points) - SHAPE_AREA: Area (units based on coordinate system) Feature Datasets - Groups of feature classes sharing the same spatial reference Ensuring Data Integrity Subtypes - Subsets of features in a feature class or table sharing the same attributes - Used for categorizing data - Stored as coded integers to prevent typos or incorrect entries Domains - Restrict data entry to ensure integrity - Two types: 1. Coded Values: Fixed list of acceptable values 2. Range Values: Specify minimum and maximum allowable values Queries Queries allow you to request specific data from a database based on defined criteria. Types of Queries 1. Select by Attribute - Based on non-spatial attributes (e.g., numeric or text values) - Examples: - Select all poles taller than 30ft - Select all cities above a certain population threshold 2. Select by Location (Spatial Query) - Based on spatial relationships between features - Key spatial relationships: - Intersects - Within a distance - Contains - Completely contains - Within - Completely within - Identical to - Boundary touches - Have their center in Query Process 1. Define selection criteria (attributes or spatial relationships) 2. Execute the query 3. Review results 4. If needed, export selection to a new feature class Remember to validate your data model, especially domains and subtypes, before running queries to ensure accurate results. Week 5 : Vector Data Components of Vector Data Spatial information: location, shape, spatial relationships Attributes: quantitative and qualitative data stored in tables Data Types and Field Types Text Fields Can store any type of data (numbers, text, etc.) Character limits vary (e.g. 254 for shapefiles, 2 GB for file geodatabases) Numeric Fields Short Integer Storage: 2 bytes (16 bits) Range: -32,768 to +32,767 (signed) or 0 to 65,535 (unsigned) Use: IDs, codes, countable attributes Long Integer Storage: 4 bytes (32 bits) Range: -2,147,483,648 to +2,147,483,647 (signed) Use: Feature IDs, large counts (e.g. population) Float Storage: 4 bytes (32 bits)  Precision: 7 significant digits Use: Moderate precision, very large or small numbers Double Storage: 8 bytes (64 bits) Precision: 15 significant digits Use: High precision, real numbers (e.g. elevation) Date Storage: 8 bytes (64 bits) Range: 1/1/100 to 12/31/9999 + HH:MM Stored in UTC, displayed in local timezone Adding/Editing Spatial Data Digitizing: manually creating new features Steps: 1. Choose layer and template 2. Add new row 3. Select shape type 4. Set snapping preferences 5. Click to add coordinates 6. Add attributes 7. Save changes Snapping Aligns new features with existing ones Prevents errors like gaps and overlaps Types: edge, vertex, point, endpoint, midpoint snapping Set tolerance for snapping distance Georeferencing  Process of aligning geographic data to a known coordinate system Used for scanned maps, aerial photos, satellite images Steps: 1. Set control points connecting raster (FROM) to aligned target data (TO) 2. Distribute control points evenly 3. Use consistent features (e.g. ground-level intersections) 4. Apply transformations (slide, rotate, shift, scale, skew) 5. Evaluate accuracy using RMSE (Root Mean Square Error) Geocoding Transforming location descriptions into geographic coordinates Components: 1. List of addresses 2. Reference data (e.g. TIGER relationship files) Parsing: Breaking addresses into component parts Linear referencing: Placing coordinates along line segments Metadata Detailed information about a dataset Key elements: Title/Description Spatial Reference Data Source Extent Attribute Information Data Quality Lineage Usage Restrictions Keywords/Tags Week 6: Geoprocessing Overview  Definition: Processing of geographic data to generate output from input features Can be automated using model builder or programming languages Used to manipulate, analyze and transform spatial data Common Geoprocessing Tools Clip Extracts input features that overlay clip features Like using a cookie cutter on larger dataset Output shape matches clip feature boundaries Only includes data from input feature, not clip feature Intersect Creates geometric intersection of input and intersect features Includes data from both input and intersect features Excludes non-overlapping parts Erase Removes parts of input feature that fall inside erase feature Keeps only parts of input outside erase boundaries Merge Combines multiple datasets into single new output Input features must be same geometry type Output depends on first input (table or feature class) Can merge tables and feature classes Split Divides input feature into separate output feature classes Split feature must be polygons Output retains input attribute fields Union Creates geometric union of all input features All features and attributes included in output Dissolve Aggregates features based on specified attributes Merges adjacent features with same attribute value  Can calculate statistics (sum, mean, min, max, etc.) Join Operations Attribute Join Links datasets based on common field in attribute tables Requires at least one shared field as join key Output combines attributes but spatial features unchanged Spatial Join Appends attributes based on spatial relationships: Closest feature Inside a feature Intersects a feature Examples: Joining school points to neighborhood polygons Associating crime incidents with police districts Connecting homes to nearest parks Join Types Inner Join: Only matching records Left Join: All records from left table, matching from right Right Join: All records from right table, matching from left Full Outer Join: All records, null for non-matches Model Builder Visual tool to automate geoprocessing workflows Strings together sequences of tools Components: Variables, Tools, Connectors Uses environment settings for consistency Model Building Steps: 1. Set goals 2. Gather data 3. Identify required tools  4. Construct model 5. Define environment settings 6. Test model 7. Iteratively adjust and modify Key Concepts to Remember Understand input and output of each geoprocessing tool Know differences between similar tools (e.g. Clip vs Intersect) Recognize appropriate join types for different scenarios Visualize how Model Builder connects tools into workflows Consider how to apply tools to solve real-world GIS problems